Abstract:
:In many longitudinal studies, evaluating the effect of a binary or continuous predictor variable on the rate of change of the outcome, i.e. slope, is often of primary interest. Sample size determination of these studies, however, is complicated by the expectation that missing data will occur due to missed visits, early drop out, and staggered entry. Despite the availability of methods for assessing power in longitudinal studies with missing data, the impact on power of the magnitude and distribution of missing data in the study population remain poorly understood. As a result, simple but erroneous alterations of the sample size formulae for complete/balanced data are commonly applied. These 'naive' approaches include the average sum of squares and average number of subjects methods. The goal of this article is to explore in greater detail the effect of missing data on study power and compare the performance of naive sample size methods to a correct maximum likelihood-based method using both mathematical and simulation-based approaches. Two different longitudinal aging studies are used to illustrate the methods.
journal_name
Stat Methods Med Resjournal_title
Statistical methods in medical researchauthors
Wang C,Hall CB,Kim Mdoi
10.1177/0962280212437452subject
Has Abstractpub_date
2015-12-01 00:00:00pages
1009-29issue
6eissn
0962-2802issn
1477-0334pii
0962280212437452journal_volume
24pub_type
杂志文章abstract::Appropriate handling of aggregate missing outcome data is necessary to minimise bias in the conclusions of systematic reviews. The two-stage pattern-mixture model has been already proposed to address aggregate missing continuous outcome data. While this approach is more proper compared with the exclusion of missing co...
journal_title:Statistical methods in medical research
pub_type: 杂志文章
doi:10.1177/0962280220983544
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journal_title:Statistical methods in medical research
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journal_title:Statistical methods in medical research
pub_type: 杂志文章
doi:10.1177/0962280206071839
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abstract::Monte Carlo evaluation of resampling-based tests is often conducted in statistical analysis. However, this procedure is generally computationally intensive. The pooling resampling-based method has been developed to reduce the computational burden but the validity of the method has not been studied before. In this arti...
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doi:10.1177/0962280216661876
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doi:10.1177/0962280218764193
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abstract::This paper reviews the application of statistical models to outbreaks of two common respiratory viral diseases, measles and influenza. For each disease, we look first at its epidemiological characteristics and assess the extent to which these either aid or hinder modelling. We then turn to the models that have been de...
journal_title:Statistical methods in medical research
pub_type: 杂志文章,评审
doi:10.1177/096228029300200104
更新日期:1993-01-01 00:00:00
abstract::Competing risks data often exist within a center in multi-center randomized clinical trials where the treatment effects or baseline risks may vary among centers. In this paper, we propose a subdistribution hazard regression model with multivariate frailty to investigate heterogeneity in treatment effects among centers...
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pub_type: 杂志文章
doi:10.1177/0962280214526193
更新日期:2016-12-01 00:00:00
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journal_title:Statistical methods in medical research
pub_type: 杂志文章,评审
doi:10.1177/096228029800700205
更新日期:1998-06-01 00:00:00
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journal_title:Statistical methods in medical research
pub_type: 杂志文章
doi:10.1177/0962280212438646
更新日期:2016-02-01 00:00:00
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journal_title:Statistical methods in medical research
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更新日期:2010-02-01 00:00:00
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更新日期:2016-12-01 00:00:00
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pub_type: 杂志文章,评审
doi:10.1177/096228020101000103
更新日期:2001-02-01 00:00:00
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journal_title:Statistical methods in medical research
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journal_title:Statistical methods in medical research
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journal_title:Statistical methods in medical research
pub_type: 杂志文章
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更新日期:2020-10-01 00:00:00
abstract::Purpose The prevalence estimates of binary variables in sample surveys are often subject to two systematic errors: measurement error and nonresponse bias. A multiple-bias analysis is essential to adjust for both biases. Methods In this paper, we linked the latent class log-linear and proxy pattern-mixture models to ad...
journal_title:Statistical methods in medical research
pub_type: 杂志文章
doi:10.1177/0962280217690939
更新日期:2018-10-01 00:00:00
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pub_type: 杂志文章,评审
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更新日期:2016-08-01 00:00:00
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journal_title:Statistical methods in medical research
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